Novelty detection (or anomaly detection) is the process by which input patterns are judged to deviate from the norm(s) in a dataset. In operation, an anomaly detection system can be used to detect an anomalous pattern, such as the presence of an unexpected object or event. Such systems can be employed in the security industry to detect concealed objects underneath a person's clothing and typically use millimeter wave image (mmW) and backscatter X-ray images. The objective in such a system is to automate the process so that a human operator is not required to look at raw data. However, a visible image does not identify concealed objects. Alternatively, raw scans (such as the mmW and backscatter X-ray images) reveal what is underneath the clothing, which is the naked appearance of subjects. In the current state of technology, human operators have to directly inspect the scans displaying human subjects essentially in a naked form. Thus, such systems cannot be made operational in public for privacy reasons.
Additionally, several recent patent applications have been filed that reveal a number of anomaly detection related systems devised within the context of medical imaging. The focus of related prior art on anomaly detection in the context of medical imaging is often on feature based anomalous/cancerous cell detection. For example, U.S. patent application Ser. No. 10/633,815, by Wrigglesworth et al., describes a system for detecting anomalous targets, namely cancerous cells. In this particular paradigm, a predefined number of features are extracted from a particular set of cell imagery. The features are then used to generate a probabilistic belief function to determine a probability that at least some of the cells in an image set are anomalous.
As another example, U.S. Pat. No. 7,072,435, issued to Metz et al., describes a system that processes computer tomography scans for detecting anomalous cancerous cells associated with lung cancer. The novelty in this particular patent is the use of CT imaging modality, while the algorithms for determining anomalies are somewhat standard computer aided detection (classification) methods.
As yet another example, U.S. patent application Ser. No. 10/352,867, by Parker et al., describes a system in which segmentation and localization of biomarkers, such as liver metastases and brain lesions, are cast as an anomaly detection problem. Statistical segmentation methods are used to identify biomarkers in the first image and the process is carried over to several subsequent images that are time evolutions of the biological construct (liver or brain tissue) that is under observation. The method relies on processing temporal sequences of three-dimensional medical imagery (instead of two-dimensional views).
The prior art described above is limited to the particular types of imagery previously described. The limitation is primarily due to availability and the need for use of such scanning modalities. Prior art in the medical imaging domain uses anomaly detection to identify cancerous lesions. In the mmW and backscatter X-ray image analysis domain, device manufacturers have made attempts at bringing together a combination of off-the-shelf algorithms to analyze images for weapon detection, yet a reliable system does not currently exist that is able to detect anomalies in variety of circumstances.
The present invention improves upon the simple imaging concept of the prior art by expanding the anomaly detection concept to incorporate population code based models that use adaptively timed learning. Such a population code model was described by Gorchetchnikov, A. and S. Grossberg in “Space, time, and learning in the hippocampus: how fine spatial and temporal scales are expanded into population code for behavior control,” Neural Networks (2006), Volume 20, pp 182-193, and by Grossberg, S. and J. Merrill in “A neural network model of adaptively timed reinforcement learning and hippocampal dynamics,” Cognitive Brain Research (1992), pp 3-38.
Previous models using a spectral population code have not been applied to novelty (anomaly) detection and do not consider a serial architecture. Some aspects of the design are derived from adaptively timed learning approaches (i.e., Grossberg and Merrill 1992; Gorchetchnikov and Grossberg 2006), and the present invention for anomaly detection is based on a computational model that conforms to anatomical (structural/static) and physiological (functional/dynamic) constraints in the mammalian brain during the detection of anomalous events. A unique aspect of this design is that it offers a trade-off in spatial or temporal resources. This makes the present invention more applicable to a wider array of computational platforms with vastly different scales. The ability to address a wider array of computational platforms, in turn, makes the present invention more relevant to a larger set of application domains requiring large scale novelty detection.
Thus, a continuing need exists for an anomaly detection system that utilizes a neural circuit design with parallel and distributed processing.